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R Lover but !a Programmer

Chuck Powell

Announcing another slopegraph plotting function -- June 14, 2018

Tagged as: [ R  ggplot2  functions  dplyr  slopegraph  CRAN  ggrepel  ]

A couple of weeks ago I wrote a blog post about slopegraphs. There was some polite interest and it was a good chance to practice my functional programming skills so I decided to see if I could make a decent R function from what I had learned. It’s in pretty good shape so I just pushed an update to CRAN (it will take awhile to process). You can also get the latest version from GitHub.

The documentation for it is here. Longer term I hope to move it here.



The package also includes other functions that I find useful for teaching statistics as well as actually practicing the art. They typically are not “new” methods but rather wrappers around either base R or other packages and concepts I’m trying to master.

  • Plot2WayANOVA which as the name implies conducts a 2 way ANOVA and plots the results using ggplot2
  • PlotXTabs which as the name implies plots cross tabulated variables using ggplot2
  • neweta which is a helper function that appends the results of a Type II eta squared calculation onto a classic ANOVA table
  • Mode which finds the modal value in a vector of data
  • SeeDist which wraps around ggplot2 to provide visualizations of univariate data.
  • OurConf is a simulation function that helps you learn about confidence intervals


# Install from CRAN

# Or the development version from GitHub
# install.packages("devtools")


Many thanks to Dani Navarro and the book > (Learning Statistics with R) whose etaSquared function was the genesis of neweta.

“He who gives up safety for speed deserves neither.” (via)

A shoutout to some other packages I find essential.

  • stringr, for strings.
  • lubridate, for date/times.
  • forcats, for factors.
  • haven, for SPSS, SAS and Stata files.
  • readxl, for .xls and .xlsx files.
  • modelr, for modelling within a pipeline
  • broom, for turning models into tidy data
  • ggplot2, for data visualisation.
  • dplyr, for data manipulation.
  • tidyr, for data tidying.
  • readr, for data import.
  • purrr, for functional programming.
  • tibble, for tibbles, a modern re-imagining of data frames.

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Contributions in the form of feedback, comments, code, and bug reports are most welcome. How to contribute:

  • Issues, bug reports, and wish lists: File a GitHub issue.
  • Contact the maintainer ibecav at gmail.com by email.
Written on June 14, 2018